Phoebe bournei is a rare and endangered plant endemic to China with higher-value uses in essential oil and structural wood production. Its seedlings are prone to death because of its undeveloped system. Paclobutrazol (PBZ) can improve root growth and development in certain plants, but its concentration effect and molecular mechanism remain unclear. Here, we studied the physiological and molecular mechanisms by which PBZ regulates root growth under different treatments. We found that, with moderate concentration treatment (MT), PBZ significantly increased the total root length (69.90%), root surface area (56.35%), and lateral root number (47.17%). IAA content was the highest at MT and was 3.83, 1.86, and 2.47 times greater than the control, low, and high-concentration treatments. In comparison, ABA content was the lowest and reduced by 63.89%, 30.84%, and 44.79%, respectively. The number of upregulated differentially expressed genes (DEGs) induced at MT was more than that of down-regulated DEGs, which enriched 8022 DEGs in response to PBZ treatments. WGCNA showed that PBZ-responsive genes were significantly correlated with plant hormone content and involved in plant hormone signal transduction and MAPK signal pathway-plant pathways, which controls root growth. The hub genes are observably associated with auxin, abscisic acid syntheses, and signaling pathways, such as PINs, ABCBs, TARs, ARFs, LBDs, and PYLs. We constructed a model which showed PBZ treatments mediated the antagonism interaction of IAA and ABA to regulate the root growth in P. bournei. Our result provides new insights and molecular strategies for solving rare plants’ root growth problems.

Forest fires are a major natural calamity that inflict substantial harm on forest resources and the socio-economic landscape. The eastern region of China is particularly susceptible to frequent forest fires, characterized by high population density and vibrant economic activities. Precise forecasting in this area is essential for devising effective prevention strategies. This research utilizes a blend of kernel density analysis, autocorrelation analysis, and the standard deviation ellipse method, augmented by geographic information systems (GISs) and deep-learning techniques, to develop an accurate prediction system for forest-fire occurrences. The deep-learning model incorporates data on meteorological conditions, topography, vegetation, infrastructure, and socio-cultural factors to produce monthly forecasts and assessments. This approach enables the identification of spatial patterns and temporal trends in fire occurrences, enhancing both the precision and breadth of the predictions. The results show that global and local autocorrelation analyses reveal high-incidence areas mainly concentrated in Guangdong, Fujian, and Zhejiang provinces, with cities like Jiangmen exhibiting distinct concentration characteristics and a varied spatial distribution of fire occurrences. Kernel density analysis further pinpoints high-density fire zones primarily in Meizhou, Qingyuan, and Jiangmen in Guangdong Province, and Dongfang City in Hainan Province. Standard deviation ellipse and centroid shift analysis indicate a significant northward shift in the fire-occurrence centroid over the past 20 years, with an expanding spatial distribution range, decreasing flattening, and relatively stable fire-occurrence direction. The model performs effectively on the validation set, achieving an accuracy of 80.6%, an F1 score of 81.6%, and an AUC of 88.2%, demonstrating its practical applicability. Moreover, monthly fire zoning analysis reveals that high-incidence areas in spring and winter are mainly concentrated in Guangdong, Fujian, Zhejiang, and Hainan, while autumn shows widespread medium-incidence areas, and summer presents lower fire occurrences in most regions. These findings illustrate the influence of seasonal climate variations on fire occurrences and highlight the necessity for enhanced fire monitoring and prevention measures tailored to different seasons.

Fertilization is widely acknowledged as being an essential practice to improve forest productivity in forest ecosystems. However, too little consideration has been given to the taxonomic and functional compositions of rhizosphere soil microbes and their interactions with root exudates under different fertilizer regimes in forest plantations. Here, we investigated the effects of four typical fertilizer regimes (CK, no fertilizer; CF, compound fertilizer; OF, organic fertilizer; CMF, compound microbial fertilizer) on soil microbial communities and their potential functional groups in Phoebe bournei young plantations, as well as their associations with soil physicochemical properties and root exudates. These results showed that fertilizer regimes strikingly affected the rhizosphere soil microbial community compositions and alpha diversity indices. The pathotroph was the dominant fungal guild. With the applications of three fertilizations, the relative abundances of the plant pathogen and arbuscular mycorrhiza increased. The alpha diversity of soil bacteria was highest under the OF regime, and soil fungal diversity was more powerfully affected by the amendment of CMF. Additionally, while the fungal community was simultaneously influenced by soil physiochemical factors and root exudates, the bacterial community in the rhizosphere was mostly impacted by root exudates. More importantly, the application of OF and CF induced dramatic growths of Fusarium, while CMF treatment including Bacillus suppressed the development of Fusarium via adjusting bacterial species. Overall, our findings exhibit the divergent responses of rhizosphere bacteria and fungi to fertilizer regimes in P. bournei young plantations. The application of organic fertilizer provides benefits for rhizosphere bacteria, and microbial fertilizer can help alleviate inhibition through changing pathogens.

In the context of global climate warming, climate change is subtly reshaping the patterns of wildfires. Therefore, it is particularly urgent to conduct in-depth research on climate change, wildfires, and their management strategies. This study relies on detailed fire point data from 2001 to 2020, skillfully incorporating a spatial autocorrelation analysis to uncover the mysteries of spatial heterogeneity, while comprehensively considering the influences of multiple factors such as climate, terrain, vegetation, and socioeconomic conditions. To simulate fire conditions under future climates, we adopted the BCC-CSM2-MR climate model, presetting temperature and precipitation data for two scenarios: a sustainable low-development path and a high-conventional-development path. The core findings of the study include the following: (i) In terms of spatial heterogeneity exploration, global autocorrelation analysis reveals a striking pattern: within the southern forest region, 63 cities exhibiting a low–low correlation are tightly clustered in provinces such as Hubei, Anhui, and Zhejiang, while 48 cities with a high–high correlation are primarily distributed in Guangxi and Guangdong. Local autocorrelation analysis further refines this observation, indicating that 24 high–high correlated cities are highly concentrated in specific areas, 14 low–low correlated cities are located in Hainan, and there are only 3 sparsely distributed cities with a low–high correlation. (ii) During the model construction and validation process, this study innovatively adopted the LR-RF-SVM ensemble model, which demonstrated exceptional performance indicators: an accuracy of 91.97%, an AUC value of 97.09%, an F1 score of 92.13%, a precision of 90.75%, and a recall rate of 93.55%. These figures, significantly outperforming those of the single models SVM and RF, strongly validate the superiority of the ensemble learning approach. (iii) Regarding predictions under future climate scenarios, the research findings indicate that, compared to the current fire situation in southern forest areas, the spatial distribution of wildfires will exhibit a noticeable expansion trend. High-risk regions will not only encompass multiple cities in Hunan, Hubei, southern Anhui, all of Jiangxi, and Zhejiang but will also extend northward into southern forest areas that were previously considered low-risk, suggesting a gradual northward spread of fire risk. Notably, despite the relatively lower fire risk in some areas of Fujian Province under the SS585 scenario, overall, the probability of wildfires occurring in 2090 is slightly higher than that in 2030, further highlighting the persistent intensification of forest fire risk due to climate change.